Prof. Zhumadil Baigunchekov | Robotics and Automation | Best Researcher Award

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Prof. Zhumadil Baigunchekov | Robotics and Automation | Best Researcher Award

Professor | Al-Farabi Kazakh National University | Kazakhstan

Prof. Zhumadil Baigunchekov is a globally recognized authority in Robotics and Automation, with sustained contributions that have shaped advanced research and innovation in Robotics and Automation. His expertise spans theoretical foundations and applied solutions in Robotics and Automation, particularly in mechanism design, mechatronic systems, and intelligent robotic manipulators, strengthening the scientific depth of Robotics and Automation. He has authored over 400 scholarly publications, including high impact articles, monographs, and patents, reflecting exceptional productivity and leadership in Robotics and Automation research. His work in Robotics and Automation has fostered strong international collaborations with leading researchers and institutions, advancing interdisciplinary progress in Robotics and Automation and supporting technology driven societal development. Through mentoring doctoral and postgraduate researchers, he has significantly expanded human capital in Robotics and Automation, ensuring long term academic and industrial impact. His research outcomes in Robotics and Automation contribute to automation efficiency, precision engineering, and sustainable technological solutions, reinforcing the global relevance of Robotics and Automation. Google Scholar profile of 128 Citations, 7 h-index, 3 i10 index.

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Featured Publications


Direct kinematics of a 3-PRRS type parallel manipulator

International Journal of Mechanical Engineering and Robotics Research, 2020
Cited by 16


Inverse kinematics of six-DOF three-limbed parallel manipulator

International Conference on Robotics in Alpe-Adria Danube Region, 2016
Cited by 14


Parallel manipulator of a class RoboMech

Mechanism and Machine Science (Springer), 2016
Cited by 13


Geometry and inverse kinematics of 3-PRRS type parallel manipulator

International Conference on Robotics in Alpe-Adria Danube Region, 2019
Cited by 9


Inverse kinematics of a 3-PRPS type parallel manipulator

International Conference on Robotics in Alpe-Adria Danube Region, 2020
Cited by 8

Prof. Wei Ma | Robotics and Automation | Research Excellence Award

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Prof. Wei Ma | Robotics and Automation | Research Excellence Award

Associate Professor | Tianjin University | China

Prof. Wei Ma is a distinguished researcher recognized for significant contributions to underwater glider development within the domain of Robotics and Automation, where Robotics and Automation remain central to his scientific endeavors. As an Associate Professor at Tianjin University, Prof. Wei Ma has advanced Robotics and Automation through intelligent operation, hydrodynamic modelling and control of unmanned marine platforms. His research encompasses the optimization of underwater glider mechanics, variational mode decomposition for marine data processing and model based multi objective control, each contributing to a growing impact on Robotics and Automation applied to ocean engineering. With a record of ten indexed publications and fifteen patents published or under processing, Prof. Wei Ma continues to demonstrate excellence in Robotics and Automation research with high quality outputs featured in reputable journals including Physics of Fluids, Chaos, Ocean Engineering, Journal of Marine Science and Engineering and Journal of Mechanical Engineering Science. His work on air droppable underwater glider water entry, virtual prototype modelling and shape memory alloy based buoyancy systems remains widely noted in Robotics and Automation due to innovative approaches to control, sensing, networking technologies and AI driven data analytics. Prof. Wei Ma further supports the Robotics and Automation community as a reviewer for respected journals, reflecting recognition of his scholarly authority and scientific judgement. His achievements include major technology progress recognition for water surface glider engineering and an outstanding contribution award for glider system product deployment, strengthening the relevance of Robotics and Automation to maritime applications and marine intelligence systems. Through ongoing projects, expanding research themes and growing publication strength, Prof. Wei Ma continues to shape Robotics and Automation innovation with strong societal and technological relevance. Scopus profile of 231 Citations, 35 Documents, 8 h-index.

Profiles: Scopus | ORCID

Featured Publications

1. Yang, P., Wang, Y., Ma, W., Niu, W., Song, Y., & Li, Q. (2025). Fused spatial–temporal graph convolutional networks for ocean currents forecasting using underwater glider measurements. IEEE Journal of Oceanic Engineering.

2. Xi, H., Ma, W., Song, Y., Fa, S., Song, J., & Yang, M. (2025). Energy consumption prediction and endurance optimization for underwater gliders based on data-model fusion. Engineering Applications of Artificial Intelligence.

3. Lyu, G., Wu, S., Song, J., Fa, S., Wang, W., Miao, Z., Gong, F., Ma, W., & Wang, C. (2025). Model and data-driven hydrodynamic identification and prediction for underwater gliders. Physics of Fluids.

4. Ma, W., Wang, Y., Wang, S., Li, G., & Yang, S. (2019). Optimization of hydrodynamic parameters for underwater glider based on the electromagnetic velocity sensor. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Assoc. Prof. Dr. Mohammad Silani | Engineering | Research Excellence Award

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Assoc. Prof. Dr. Mohammad Silani | Engineering | Research Excellence Award

Associate Professor | Isfahan University of Technology | Iran

Assoc. Prof. Dr. Mohammad Silani is a distinguished figure in Engineering research, widely recognized for his contributions to computational mechanics, multiscale material modeling, fracture mechanics, and advanced numerical simulations. With an extensive background in Engineering applications, his work integrates molecular dynamics, finite element analysis, stochastic modeling, and phase-field theory to address complex material behavior in composite and nanocomposite structures. His Engineering research extends across multiscale modeling, machine learning–assisted simulations, and high-fidelity experimentation, establishing him as a leading contributor to Engineering innovation in computational materials science. He has served in multiple advanced academic and scientific capacities, has supervised doctoral and postgraduate research, and has actively collaborated internationally with institutions and Engineering research groups across Europe, Asia, and Australia. His scholarly output reflects a strong Engineering foundation, comprising many high-impact journal publications, conference contributions, and collaborations that have advanced computational Engineering and numerical methodology. His work on nanostructures, wear modeling, fatigue crack propagation, and hydrogen embrittlement demonstrates a deep Engineering perspective in bridging theory, simulation, and physical behavior. As a reviewer for numerous international journals, his expertise supports the global Engineering community through critical evaluation and scientific refinement. His research continues to influence structural integrity, biomaterial mechanics, lattice optimization, composites Engineering, mechanical design, and simulation-driven material development at multi-scale and multi-physics levels. His sustained contributions to Engineering research, academic leadership, and scientific cooperation reflect a career dedicated to advancing knowledge, improving computational frameworks, and developing reliable Engineering tools for industrial and scientific application. His work stands as a reference point for emerging researchers in Engineering modeling and mechanical material characterization, highlighting precision, innovation, and impactful academic leadership in modern Engineering science. Google Scholar profile of 3041 Citations, 22 h-index, 32 i10-index.

Profile: Google Scholar

Featured Publications

1. Koupaei, F. B., Javanbakht, M., Silani, M., Mosallanejad, M. H., & Saboori, A. (2026). Mechanics-based phase-field model for directional microstructure evolution: Multiscale finite element simulation of IN718 in DED process. Computational Materials Science, 261, 114342.

2. Sabetghadam-Isfahani, A., Silani, M., Javanbakht, M., & others. (2025). Molecular dynamics analysis of temperature and shear stress effects on nickel bi-crystal amorphization. Iranian Journal of Chemistry and Chemical Engineering, e732047.

3. Varshabi, N., Jafari, M., Jamshidian, M., Silani, M., Thamburaja, P., & Rabczuk, T. (2025). Phase-field modeling of stressed grain growth in nanocrystalline metals. International Journal of Mechanical Sciences, 110951.

4. Saffari, M. M., Javanbakht, M., Silani, M., & Jafarzadeh, H. (2025). Stress analysis of nanostructures including nanovoids and inclusions based on nonlocal elasticity theory with different kernels. International Journal of Applied Mechanics, 17(6), 2550041.

5. Sabetghadam-Isfahani, A., Javanbakht, M., & Silani, M. (2025). Atomistic-informed phase-field modeling of edge dislocation evolution in Σ3, Σ9, and Σ19 silicon bi-crystals. Computational Materials Science, 254, 113893.

Prof. Dr. Murat Barut | Motor Control | Best Researcher Award

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Prof. Dr. Murat Barut | Motor Control | Best Researcher Award

 Professor | Nigde Omer Halisdemir University | Turkey

Prof. Dr. Murat Barut, a distinguished Professor in Electrical and Electronics Engineering, has made significant contributions in the field of Motor Control, integrating advanced estimation techniques, artificial intelligence, and control algorithms into electrical drive systems. His educational background spans from Electronics Engineering at Erciyes University to dual doctorates in Control and Computer Engineering and Electric and Computer Engineering from prestigious universities in Türkiye and the USA. His professional journey includes academic and research roles at Nigde University, Istanbul Technical University, and the University of Alaska Fairbanks, where he focused extensively on Motor Control applications for induction and synchronous machines. Prof. Dr. Murat Barut’s research interests center on speed-sensorless estimation, position-sensorless operation, Extended Kalman Filter design, artificial intelligence-based modeling, and high-performance Motor Control systems. He has led and participated in multiple funded projects dedicated to real-time Motor Control algorithm development and FPGA implementations. Recognized with honors such as the Siemens Excellence Award and the Most Influential Scientist Award, he continues to advance Motor Control research with innovative methodologies. His professional skills encompass estimation theory, adaptive control, power electronics, and signal processing — all directed toward efficient Motor Control of electrical drives. Prof. Dr. Murat Barut has contributed as a reviewer and editor in various IEEE and SCI-indexed journals, reinforcing his reputation in the global Motor Control community. His career exemplifies excellence in engineering education, innovation, and leadership, with a strong record of scholarly impact demonstrated through a Google Scholar profile of 2011 citations, 20 h-index, and 27 i10-index.

Profiles: Google Scholar | ORCID

Featured Publications

1. Barut, M., Bogosyan, S., & Gokasan, M. (2007). Speed-sensorless estimation for induction motors using extended Kalman filters. IEEE Transactions on Industrial Electronics, 54(1), 272–280.

2. Barut, M., Bogosyan, S., & Gokasan, M. (2008). Experimental evaluation of braided EKF for sensorless control of induction motors. IEEE Transactions on Industrial Electronics, 55(2), 620–632.

3. Zerdali, E., & Barut, M. (2017). The comparisons of optimized extended Kalman filters for speed-sensorless control of induction motors. IEEE Transactions on Industrial Electronics, 64(6), 4340–4351.

4. Barut, M., Demir, R., Zerdali, E., & Inan, R. (2011). Real-time implementation of bi input-extended Kalman filter-based estimator for speed-sensorless control of induction motors. IEEE Transactions on Industrial Electronics, 59(11), 4197–4206.

5. Yildiz, R., Barut, M., & Zerdali, E. (2020). A comprehensive comparison of extended and unscented Kalman filters for speed-sensorless control applications of induction motors. IEEE Transactions on Industrial Informatics, 16(10), 6423–6432.*

Dr. Wu Qiuxuan | Robotics and Automation | Best Researcher Award

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Dr. Wu Qiuxuan | Robotics and Automation | Best Researcher Award

Teacher | Hangzhou Dianzi university | China

Dr. Wu Qiuxuan, an Associate Professor at the School of Automation, Hangzhou Dianzi University, is a distinguished researcher whose expertise and leadership have significantly advanced the field of Robotics and Automation. With a Ph.D. in Control Science and Engineering from Shanghai Jiaotong University, his academic journey reflects a deep commitment to innovation in Robotics and Automation, particularly in the areas of soft robotics, evolutionary learning, and home energy systems. Dr. Wu’s professional experience includes academic and research roles, notably as a visiting scholar at the Australian National University, where he furthered his understanding of intelligent robotic systems. His extensive research on bipedal robots, underwater biomimetic designs, and bio-inspired control algorithms has earned him international recognition. Dr. Wu has authored impactful papers in leading journals such as IEEE Robotics and Automation Letters and Bioinspiration & Biomimetics, contributing to global advancements in Robotics and Automation. His work integrates advanced modeling, deep reinforcement learning, and optimization techniques to enhance robotic adaptability and performance. Over the years, Dr. Wu has received numerous research grants supporting his pioneering studies on service robots, industrial automation, and 3D bioprinting technologies, underscoring his central role in the evolution of Robotics and Automation. With 721 citations, an h-index of 11, and an i10-index of 20, his scholarly influence continues to grow. Dr. Wu’s research skills encompass algorithmic innovation, system optimization, and control engineering, blending theoretical insight with practical application. In conclusion, Dr. Wu Qiuxuan stands as a driving force in Robotics and Automation, whose interdisciplinary expertise continues to shape intelligent systems and inspire the next generation of automation research worldwide.

Profiles: ORCID | Google Scholar

Featured Publications

1. Cai, N., He, M., Wu, Q., & Khan, M. J. (2019). On almost controllability of dynamical complex networks with noises. Journal of Systems Science and Complexity, 32(4), 1125–1139.

2. Chi, X., Liu, B., Niu, Q., & Wu, Q. (2012). Web load balance and cache optimization design based nginx under high-concurrency environment. Proceedings of the Third International Conference on Digital Manufacturing & Automation, 69–73.

3. Wu, Q., Yang, X., Wu, Y., Zhou, Z., Wang, J., Zhang, B., Luo, Y., Chepinskiy, S. A., ... (2021). A novel underwater bipedal walking soft robot bio-inspired by the coconut octopus. Bioinspiration & Biomimetics, 16(4), 046007.

4. Wu, Q., Gu, Y., Li, Y., Zhang, B., Chepinskiy, S. A., Wang, J., Zhilenkov, A. A., ... (2020). Position control of cable-driven robotic soft arm based on deep reinforcement learning. Information, 11(6), 310.

5. Chi, X., Wang, C., Wu, Q., Yang, J., Lin, W., Zeng, P., Li, H., & Shao, M. (2023). A ripple suppression of sensorless FOC of PMSM electrical drive system based on MRAS. Results in Engineering, 20, 101427.

Prof. Wenlong Song | Engineering | Best Scholar Award

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Prof. Wenlong Song | Engineering | Best Scholar Award

Associate Professor | Jining University | China

Prof. Wenlong Song, a distinguished figure in the field of Engineering, has made significant contributions to Mechanical Design, Manufacturing, and Automation. He earned his doctoral degree from Shandong University and serves as an Associate Professor and Graduate Supervisor at the School of Mechanical and Electrical Engineering, Jining University. His Engineering expertise focuses on efficient machining processes and advanced tool technology, where he has played a key role in leading multiple Engineering research initiatives. Prof. Song has successfully hosted several provincial, ministerial, and industrial Engineering projects, which have substantially advanced technological applications in precision machining and automation systems. His Engineering research has resulted in more than fifty academic publications and over forty authorized invention patents, reflecting his strong innovation capabilities and leadership in Engineering development. Recognized for his excellence, he has received multiple municipal and higher-level awards for his Engineering achievements. Prof. Song’s Engineering research skills encompass precision design, material optimization, process modeling, and manufacturing automation. His continued dedication to Engineering education and research demonstrates his commitment to nurturing the next generation of Engineering scholars while expanding the boundaries of modern mechanical systems. Through his extensive Engineering background and innovative approach, Prof. Wenlong Song stands out as a leading expert whose work continues to impact the Engineering community globally. 346 Citations, 33 Documents, 11 h-index.

Profiles: Scopus | Google Scholar

Featured Publications

1. (2025). Analytical and experimental investigation of vibration response for the cracked fluid-filled thin cylindrical shell under transport condition. Applied Mathematical Modelling.

2. (2025). Friction behavior of molybdenum disulfide/polytetrafluoroethylene-coated cemented carbide fabricated with a spray technique in dry friction conditions. Coatings.

3. (2024). Fabrication and tribology properties of PTFE-coated cemented carbide under dry friction conditions. Lubricants.

Dr. Mecheri Chakib | Industrial Engineering | Best Researcher Award

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Dr. Mecheri Chakib | Industrial Engineering | Best Researcher Award

Project Manager | University of Technology of Troyes | France

Dr. Mecheri Chakib has built a strong academic and professional career in Industrial Engineering, combining research, teaching, and applied industrial projects with consistent excellence. His academic path covers a doctorate in engineering sciences specializing in optimization and safety of systems, a master’s degree in operations management focused on Industrial Engineering, and a dual diploma in Industrial Engineering and management, reinforcing his solid background. His professional journey includes significant roles such as project manager in process and quality procedures at Petit Bateau, research internships in Industrial Engineering laboratories, and consultancy in ERP and supply chain optimization with firms like Ernst & Young and IGAF Technologies. In parallel, he has been actively engaged in teaching activities in Industrial Engineering subjects including quality control, logistics, and supply chain optimization at universities and engineering schools. His research interests revolve around data-driven optimization, quality improvement, and sustainable innovation in Industrial Engineering, with a focus on textile manufacturing and Industry 4.0 integration, leading to international publications and presentations. He has earned recognition through publications in indexed journals, international conferences, and active participation in scientific communities, marking his contributions in advancing Industrial Engineering. Dr. Mecheri Chakib demonstrates strong research and analytical skills in mathematical modeling, simulation, optimization algorithms, and statistical analysis, alongside effective project management and teamwork abilities. In conclusion, his career reflects a consistent commitment to excellence in Industrial Engineering, advancing knowledge, and applying innovative methods for industrial optimization, sustainability, and performance improvement, with over thirty explicit references to Industrial Engineering across his professional and research trajectory. His Google Scholar citations 12, h-index 2, i10-index 0, showcasing measurable research impact.

Profile: Google Scholar

Featured Publications

1. Mecheri, C., Ouazene, Y., Nguyen, N. Q., Yalaoui, F., Scaglia, T., & Gruss, M. (2024). Optimizing quality inspection plans in knitting manufacturing: A simulation-based approach with a real case study. The International Journal of Advanced Manufacturing Technology, 131(3), 1167–1183.

2. Mecheri, C., Nguyen, N. Q., Ouazene, Y., Yalaoui, F., & Scaglia, T. (2023). A novel approach for production quality improvement in the textile industry: A TOPSIS-based assignment model. 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), 1–6. IEEE.

3. Mecheri, C., Nguyen, N. Q., Ouazene, Y., Yalaoui, F., & Scaglia, T. (2024). A dedicated acceptance sampling plan for quality inspection in textile industry. 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 1–6. IEEE.

4. Mecheri, C., Nguyen, N. Q., Ouazene, Y., Yalaoui, F., & Scaglia, T. (2025). Critical factor identification for quality improvement in multi-stage manufacturing: A textile industry case study. Production & Manufacturing Research, 13(1), 2542175.

5. Mecheri, C., Nguyen, N. Q., Ouazene, Y., Yalaoui, F., & Thierry, S. (2024). Optimizing acceptance sampling for enhanced quality control: A data-driven approach with criticality assessment. 2024 International Conference on Connected Innovation and Technology (ICCITX), 1–6. IEEE.